Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2015-004
September, 2015
Toward an International Comparison of Economic and Educational Mobility:
Recent Findings from the Japan Child Panel Survey
Hideo Akabayashi＊
Ryosuke Nakamura＊＊
Michio Naoi＊
Chizuru Shikishima＊＊＊【Abstract】
In past decades, income inequality has risen in most developed countries. There is growing interest among
economists in international comparisons of economic and educational mobility. This is aided by theavailability of internationally comparable, large-scale data. The present paper aims to make threecontributions. First, we introduce the Japan Child Panel Survey (JCPS), the first longitudinal survey ofschool-age children that includes cognitive and non-cognitive measures, and rich household information.
The JCPS was developed to measure dynamic inter-relationships between children’s academic and social
outcomes, their family background, and local policy and environment, in a way that allows comparison of theresults with international data. Second, based on JCPS data, we present selected results of the dynamics ofinequality in multiple indicators of children’s educational and behavioral outcomes. We found that changes
in cognitive achievement across parental income groups, the degree of mobility of cognitive test scores, andthe correlation between the difficulty score and parental education in Japan are similar to other countries,
such as the United States, United Kingdom, Australia, and Germany. Finally we discuss issues underlying
the globalization of education research based on our experiences with the JCPS. We discuss reasons andstrategies for further globalization of education research in Japan, and propose suggestions as to how
Japanese education research can move toward better international collaboration, particularly in research on
economic and educational mobility.
＊

Keio University＊＊

Fukuoka University＊＊＊

Teikyo University

Panel Data Research Center at Keio University
Keio University

Toward an International Comparison of Economic and Educational Mobility:
Recent Findings from the Japan Child Panel Survey
Hideo Akabayashi (Keio University)
Ryosuke Nakamura (Fukuoka University)
Michio Naoi (Keio University)
Chizuru Shikishima (Teikyo University) *September 27, 2015
JEL classification: D31, I24, J13
Keywords: Economic Inequality; Educational Inequality and Mobility; Panel Data;
Cognitive and Non-cognitive Abilities
Abstract

In past decades, income inequality has risen in most developed countries. There is growinginterest among economists in international comparisons of economic and educational
mobility. This is aided by the availability of internationally comparable, large-scale data.
The present paper aims to make three contributions. First, we introduce the Japan Child
Panel Survey (JCPS), the first longitudinal survey of school-age children that includes
cognitive and non-cognitive measures, and rich household information. The JCPS wasdeveloped to measure dynamic inter-relationships between children’s academic and social
outcomes, their family background, and local policy and environment, in a way that allows
comparison of the results with international data. Second, based on JCPS data, we present
selected results of the dynamics of inequality in multiple indicators of children’s
educational and behavioral outcomes. We found that changes in cognitive achievement
across parental income groups, the degree of mobility of cognitive test scores, and thecorrelation between the difficulty score and parental education in Japan are similar to othercountries, such as the United States, United Kingdom, Australia, and Germany. Finally we
discuss issues underlying the globalization of education research based on our experienceswith the JCPS. We discuss reasons and strategies for further globalization of education
research in Japan, and propose suggestions as to how Japanese education research can movetoward better international collaboration, particularly in research on economic andeducational mobility.
email: Akabayashi: hakab@econ.keio.ac.jp, Nakamura: rnakamura@fukuoka-u.ac.jp,
Naoi: naoi@econ.keio.ac.jp, Shikishima: shiki@main.teikyo-u.ac.jp. We used individual
data drawn from the Keio Household Panel Survey (KHPS), the Japan Household Panel
Survey (JHPS), and the Japan Child Panel Survey (JCPS) from the Panel Data
Research Center at Keio University. This research was financially supported by aGrant-in-Aid for Specially Promoted Research (24000003), a Grant-in-Aid for Scientific
Research (B) 24330090, and the JSPS Topic-Setting Program to Advance Cutting-Edge
Humanities and Social Sciences Research (Responding to Real Society). We would liketo thank Yoshio Higuchi for continuous support of our survey and research. Part of thispaper was previously presented at the 20th International Panel Data Conference(IPDC), Tokyo, under the title “Dynamics of Educational Inequality among Children in
Japan: Findings from the First Four Years of the Japan Child Panel Survey.” We also
thank all colleagues involved in the JCPS project, especially the co-authors of the IPDC
presentation, Jun Yamashita, Kayo Nozaki, Shinpei Sano, and Wataru Senoo. The views
expressed here and possible errors are solely the authors’ own.
*

1

1. Introduction
1.1. Growing Interest in Economic and Educational Mobility

It is well-known that income inequality has risen in most developed countries overthe past decades (OECD, 2015). As this rising inequality is increasingly recognizedamong policy makers, social scientists, and the general public, discussion has evolved
about why widening income inequality has occurred and what kinds of policy could stop
this trend (Goldin & Katz, 2008; Piketty, 2014).
Economic theory suggests that rich families tend to have better access to financial
resources for private education of their children than poor families, and that motivation
for private education can create forces that widen the achievement gap between richand poor over generations (Becker, 1967). Therefore, most social scientists have viewed
the development of high-quality public education systems as a key mechanism throughwhich any society may move to a more equal society over generations. Long-term trends
in widening inequality have triggered concerns in many countries about whether public
education has been truly effective in narrowing the gap between children from wealthyfamilies and children from low-income families (Hanushek, Peterson, & Woessmann,2013). If the public school system is ineffective, income inequality tends to betransmitted over generations through an education gap, widening income inequality
and strengthening low intergenerational mobility.
Given such concerns, the economics of education has recently focused on two major
issues: first, whether and what kind of public school system is effective in enhancing
children’s cognitive and non-cognitive ability, and their future economic and social
well-being; and second, whether and how family resources are related to various
dimensions of children’s educational achievement during the course of childdevelopment. In the past decade, both issues have been intensively examined worldwide,
largely because of increasing awareness of the limitations of only looking at one
economic system in one country, and the availability of internationally comparable
educational datasets.
One notable example is a cross-country scatter plot of income inequality andintergenerational mobility measured by the intergenerational elasticity of earnings,
created by Corak (2013). This scatter plot was called “the Great Gatsby Curve” by Alan
Krueger, then Chairman, Council of Economic Advisers to President Obama (Figure 1).
(Insert Figure 1 about here)
The curve shows that some developed countries, especially the United States, areamong the most unequal societies and also among the countries with the fewest2

opportunities for children in poor families. This is the opposite of the long-held andwidely-accepted belief that America is the “Land of Opportunity” (Duncan & Murnane,2011, p.3).
Discussion has occurred about the reliability of the estimates in the curve that use
cross-country data, underlying mechanisms that could have created this curve, andpotential policy actions that may generate better chances for future generations,particularly in the countries located at the top-right of the curve, with greater
inequality and less equal opportunities for children. Researchers and policy makers inthose countries have been directing attention to the education systems in the countries
in the left-bottom of the curve, with less inequality and more opportunities for children(Hanushek & Woessman, 2015; Ripley, 2013).
This has been aided by the availability of internationally comparable large-scale
educational data collected by international organizations, such as the Programme forInternational Student Assessment (PISA) and the Trends in International Mathematics
and Science Study (TIMSS). However, as the currently available internationallycomparable education data are all cross-sectional, there is emerging international
research cooperation that aims to make domestic longitudinal data for children ascomparable as possible, to gain insights into the differences in the degree of the effectsof family resources on educational inequality, and the associated policy implications(Ermisch, Jäntti, & Smeeding, 2012a; Smeeding, Erikson, & Jäntti, 2011).
1.2. Aim of the Present Paper: Educational Mobility in Japan Using Longitudinal Data
Japan should not be an exception to this cooperation, as Japan also faces concerns
about increasing inequality. Although previously considered to be an equality-oriented
society, research over the past 15 years found that there was emerging and increasing
inequality in Japanese society (Tachibanaki, 2006).
A widening gap in children’s and students’ academic ability has recently been
identified as a serious problem in Japan (Park & Lee, 2013). Household socioeconomic
factors have been highlighted as a possible explanation for this widening gap. In
previous surveys of academic ability, a child’s academic ability was found to be affectedby his or her parents’ educational attainment and income (Kariya & Shimizu, 2004).
The percentage of questions answered correctly by children who did not study in any
place other than a school (“No study kids”) was also found to be low. It has beensuggested that the impact of school teaching on a child’s academic ability has decreased,
whereas the impact of the child’s home environment has increased (Kariya, 2008, p.37).
A recent study based on the National Assessment of Academic Ability, a nation-wide3

cognitive test of Japanese and mathematics that assessed children in grades 6 and 9,
found that parental income and education had a positive association with both scores inboth grades (Hamano, 2014).
As the home environment has an influence on children’s academic ability, recentsocial changes such as increasing socioeconomic inequality and poverty may haveresulted in the accelerated decline in academic ability and the widening academic
ability gap across households with different family backgrounds. Therefore, to address
the issues of the decline in academic ability and the increase in academic ability gap, it
may be important to clarify the impact of household socioeconomic factors such as
parents’ educational attainment and income on children’s academic abilities, as well as
the dynamic process of this impact.
The importance of using longitudinal data to examine the role of family resourcesin shaping children’s educational outcomes has long been recognized. For example,
Carneiro and Heckman (2003) provided an early account of how educational inequality
such as test scores, evolved during child development across income groups in the
United States. More recently, Duncan and Murnane (2011) documented the changes in
the relationship between socioeconomic status (SES) of families and an array ofchildren’s educational and social outcomes such as test scores, academic achievement,
and behaviors and attitudes of children over their life-course and over generations.
However, in Japan, the lack of longitudinal data for children’s academic andnon-academic outcomes has hindered researchers in comprehensively investigating
dynamic interrelationships between family, educational investment, and children’s
outcomes.1
The present paper aims to make three contributions. First, we introduce theJapan Child Panel Survey (JCPS), Japan’s first longitudinal survey of school-age
children, including cognitive and non-cognitive measures, and rich household
information. We conceived, designed, and developed this dataset with the help of manyother researchers, to measure dynamic interrelationships between children’s academic
and social outcomes, their family background, and local policy and environment. We
also explain the survey design of the JCPS that makes the dataset potentiallycomparable to major datasets available in other developed countries, and theconstruction of key variables used in the present analyses.
Second, we present some of our recent findings on the dynamics of inequality
using multiple indicators of children’s educational and behavioral outcomes based on
Ikesako and Miyamoto (2015) discuss research findings using longitudinal data forchildren in Japan, including the JCPS.

1

4

the JCPS.2 Previously, Akabayashi et al. (2013) presented evidence of the correlation
between a child’s attributes and home environment and that child’s academic abilities.
In the present paper, taking advantage of the recent accumulation of multiple
observations of the same children, we take the analysis a step further and examine thedynamics of the correlations between family socioeconomic conditions and a child’s
academic outcomes. We also compare our results with those from other countries, andprovide a brief discussion about the differences and similarities, as well as the issues
involved in international comparisons of this kind.
Finally, we discuss some issues underlying the globalization of education research,based on our JCPS experiences. We consider the growing interest in international
comparisons of economic and educational mobility, and discuss possible reasons andstrategies for further globalization of education research in Japan. We also suggest
several potential future directions for Japanese education research, to allow betterinternational comparisons of economic and educational mobility of children.
2. Japan Child Panel Survey: Design and Measurement
The JCPS is a longitudinal parent-child survey initiated in 2010 by the Panel Data
Research Center (PDRC) at Keio University. It was designed as a supplement module to
the Japan Household Panel Survey (JHPS) and the Keio Household Panel Survey(KHPS), two comprehensive household surveys initiated in 2004 (KHPS) and in 2009
(JHPS).
The JCPS participants were parents of children enrolled in elementary (grades 1–
6; aged 6–12 years) or junior high school (grades 7–9; aged 12–15 years), as well as thechildren themselves. The PDRC conducts JCPS surveys with the JHPS and KHPS adultsamples on alternate years.3 Participants in the JHPS 2010 were invited to participate
in the first JCPS survey (2010). The second JCPS survey (2011) targeted participantsfrom the KHPS 2011, the third JCPS survey (2012) targeted participants from the
JHPS 2012, and the fourth JCPS survey (2013) involved participants from the KHPS
2013.4 Figure 2 summarizes the JCPS, JHPS, and KHPS timeline structure.
(Insert Figure 2 and Table 1 here)
Table 1 shows the potential number of households/children who could participate
Some of these results are prepared for our forthcoming monograph, Akabayashi,
Shikishima, & Naoi (forthcoming).
3 The JCPS structure is similar to the Children of National Longitudinal Survey ofYouth.
4 For an overview of the JCPS 2012, see Shikishima (2013).
2

5

in the JCPS, the number of households/children who actually participated, and the
response rates by household and child units, respectively.
The JCPS survey consists of separate children’s and parents’ forms. The children’s
form includes basic academic ability tests in Japanese and mathematics (Shikishima,
Naoi, Yamashita, & Akabayashi, 2013), logical reasoning tests, and a questionnaire
relating to school, studies, and subjective quality of life (QOL). The Japanese andmathematics questions differed for each grade. 5 The same academic ability test
batteries were used in each of the different JCPS years. Children were asked tocomplete the academic ability test by themselves within 20 min., and complete thequestionnaire independently after completing the test. The questionnaire included
questions on afterschool activities, favorite and least favorite study subjects, school life,and homework situation. The instructions specified that the child him/herself should
immediately seal the completed survey form using the enclosed seal and then hand it tohis/her parent.
Parents completed the same questionnaire irrespective of their child’s grade. The
parent’s questionnaire included items such as the type of school that their childattended, the size of the class that their child belonged to, experience of an entrance
examination for a private or national school, the time the child spent studying, theactual household expenditure on education, parenting style, and their child’s sociality
and problem behaviors. Parents who had two or more children in the survey were asked
to respond to an individual questionnaire for each child.
The children’s outcome variables analyzed were: Academic ability test scores forJapanese and mathematics (cognitive ability), and questionnaire scores for behavioral
difficulties (non-cognitive ability). The Japanese test consisted of vocabulary, andreading and writing of kanji characters. The mathematics test consisted of calculations
and questions expressed in words concerning numbers and the manipulation of figures.
The reliability and validity of the academic ability test was verified elsewhere (fordetails of the academic ability test, see Shikishima et al., 2013). The internalconsistency of each grade’s test using Cronbach’s α ranged from 0.84 to 0.94 (M=0.88)
for Japanese and 0.76 to 0.93 (M=0.87) for mathematics.
Children’s behavioral difficulties were assessed based on parental responses on
the Strengths and Difficulties Questionnaire (SDQ) (Goodman, 1997) for each child. The
SDQ is a 25-item Likert-style psychometric scale that asks parents to rate children’sdifficulties

on

four

subscales:emotional

symptoms,

conduct

problems,

Therefore, the test scores are not comparable across grades. We are planning to makegrade-standardized test scores using item response theory (IRT) in future.

5

6

hyperactivity/inattention, and peer relationship problems. Each subscale is measured
with five items, with the summed score of the five items forming the subscale score. A
total difficulties score is derived from the summed scores of the subscales (based on 20of the 25 items). This questionnaire has been used by Japan’s Ministry of Health,Labour, and Welfare as a continuous variable scale to screen children for problematic
behaviors and to identify mild developmental disorders (Matsuishi et al., 2008). The
reliability and validity of the scale has been confirmed (Stone, Otten, Engels, Vermulst,& Janssens, 2010). The internal consistency for the total score of our sample(Cronbach’s α was 0.77.6
Individual-level datasets from the JCPS, JHPS, and KHPS are available tointernational researchers for general research purposes.7
Currently, children over 18 years of age who formerly participated in the JCPS are
being resurveyed, with a variety of social, economic, and psychological outcomes
expected to be measured. These data will be incorporated into the current JCPS data
files, to allow empirical clarification of how developmental process and family
environments during school age affects the individual’s later outcomes in adolescenceand adulthood.
3. Empirical Analysis of Cognitive/Non-cognitive Achievement and Family BackgroundAt the time of the present analysis, we had only two waves of survey data for all
cohorts of children. Therefore, we pooled the cohorts and analyzed changes in the
distribution of outcome variables across groups over the two waves. To overcome thesmall sample size, we pooled children of two grades to form groups to compute averageoutcomes. For example, the distribution of outcomes for children in grades 1–2 acrossgroups was compared with the distribution of outcomes two years later (outcomes forgrades 3–4). Therefore, we had three different groups of children: grades 1–2, 3–4, and
5–6 in the base year, with a two-wave panel structure. This formed the basis of our

The JCPS also measures educational investment by the parent, including monetary
expenditure in several categories (tuition, allowances, and extra-curricular study costs);
frequency of the child’s extra-curricular activities (arts, sports, study excluding
cram-school, and cram-school). There are other behavioral and health outcomes such ashours of study (answered by the parent), height and weight at present and at birth. The
JCPS also includes the subjective QOL of children enrolled in grades 3 and beyond
through self-report. See Akabayashi, Naoi, & Shikishima (forthcoming) for analyses
using these variables.
7 The JCPS 2010–2012 files are currently available with questionnaires in English. See
http://www.pdrc.keio.ac.jp/en/ for detailed information.
6

7

sample data.
The outcome variables of interest were (1) cognitive test scores (Japanese andmathematics), and (2) non-cognitive questionnaire scores (SDQ total difficulty score).
For the cognitive measures, we transformed the individual total test scores to factor
scores using categorical factor analysis.8 We also applied standardization at grade level
whenever possible for the purpose of comparability.
In the present paper, we present selected results that demonstrate the value of the
JCPS in terms of its comparability to previously published results from other countries(Ermisch et al., 2012a). With regard to the test scores, we first examine the dynamics ofthe test scores across households with different levels of income. We then examine the2-year mobility of test scores. Finally, we present the results of the association between
the total difficulty score and parental education background.
For household income groups, we used household gross income quartiles surveyed
in the base year of the 2-year panel structure. For parental education background, weused the International Standard Classification of Education (ISCED) to achieve
comparability across countries. Any international comparison using different national
data sources has obvious limitations, and difficulties and possibilities are notedwhenever applicable.9
3.1. Dynamics of Cognitive Test Score Inequality
Figure 3 (1) shows the dynamics of the average Z-scores 10 for Japanese andmathematics tests for the top and bottom income quartiles for the grade-based groups ofchildren (grades 1–2, grades 3–4, and grades 5–6) in the JCPS base survey year. The
vertical axis shows standardized Z-scores and the horizontal axis the groups of children.
The lines are connected for children in the same family income quartiles in the same
grade-based group.11
This indicates that the test score gap tends to widen across income groups overgrades. The pattern of the changes is similar for the Japanese and mathematics scores,

For details of the academic ability test score, see Shikishima et al. (2013).
As the sample weights of children were not available at the time of writing, ourmobility calculation was based on the raw distribution of our sample.
10 Individual factor scores were transformed to Z-scores, with a mean of 50 and astandard deviation of 10.
11 An alternative choice is to define the quartiles based on the contemporaneous income
level, which has an advantage in being applicable to both cross-section and panel data
and to precisely reflect the contemporaneous income inequality. Our current definition
exploits the structure of panel data, and has an advantage in fixing unobserved
household and child characteristics.
898

with a negligible score gap for children in grades 1–2, but a widening gap toward grades
3–4 that continues to widen, albeit to a lesser extent, toward grades 5–6. However, thegap tends to shrink toward grades 7–8. The score gap between the top and bottom
income quartiles is the widest at grades 5–6.
In comparison, Figures 3 (2) and (3) show the dynamics of the test scores forreading and mathematics for income quintile groups constructed from longitudinal datafrom the United Kingdom and United States (Magnuson, Waldfogel, & Washbrook,2012). Although exact comparability across different countries is not possible because ofdifferences in the test items, the similarities in the test score gaps across income groups
between the two countries are interesting, and the gap appears to be more stable acrossthe ages than in Japan. Moreover, the gap appears to increase continuously from 7
years of age toward 14 years of age. This differs from Japan, where the gap appears tonarrow around ages 11 to 14 years.
Figure 4 shows graphical images of the transition matrix of cognitive outcomes for
two years in three countries: Japan, the United Kingdom, and Australia. The Japanesefigure was created for children who were in grades 7–8 in the base year. We calculated
the 4 × 4 transition matrices for Japanese and mathematics test scores, and used theaverage of the two transition matrices for the figure for comparability to the other two
countries. Figure 4 (1) shows the two-year transition probabilities from the first(bottom) quartile to each quartile, and Figure 4 (2) shows the same transition
probability from the fourth (top) quartile to each quartile. The United Kingdom (age 7
years) and Australia (ages 6 to 7 years) figures were drawn from Blanden, Katz, andRedmond (2012). At a glance, the curves are strikingly similar across the countries,
although the dataset in each country includes different test items. The transition
probability to remain in the bottom quartile ranges from 46.2% to 55.9%, with theUnited Kingdom showing the highest persistence. The transition probability to remainin the top quartile ranges from 45.9% to 52.0%, and again the United Kingdom shows
the highest persistence.
3.2. Correlation Between Difficulty Scores and Parental Education
Figure 5 shows the mean Z-scores for the SDQ total difficulty score (a negativevalue is more desirable) by parental education and parental income quartiles for Japan.
The comparable figures were created for the United Kingdom, and Germany by Ermisch,
Frauke, & Spiess (2012b)12. The comparability of outcome variable is maintained as all
The ages of the children in the United Kingdom and Germany were 5 and 6 years,
whereas we used children aged 7 and 8, the youngest children in the JCPS. The figures129

countries used similar questionnaires. 13 To maintain the comparability of parental
education level across different higher education institutional settings, we followed
Ermisch et al. (2012b), and defined four education level categories in Japan using the
UNESCO-ISCED classification table.14
We found that the mean standardized difficulty scores by parental education are
similar across the three countries, with the gap being the largest in the United Kingdom
and the smallest in Japan. The average scores by parental income in Japan show adifferent structure from those in the United Kingdom and Germany. The mean scoresshows a monotonic decline as the level of parental income increased in the two
European countries. However, the mean scores in Japan are not monotonically related
to parental income levels, and the score gap between income groups is smaller for Japan.
This suggests that economic condition seems less important than parental education interms of affecting children’s problematic behavior in Japan.
4. Growing Importance and Current Limitation of International Comparison of
Educational Mobility
There are several factors that have influenced increasing interest among
economists in international comparisons of economic and educational inequalities.
First, there is growing interest in the role of institutions as a factor affecting
economic and social outcomes. Traditional neoclassical economics have relied heavily on
a prototypical model of market, applying it as an approximation to countries withdifferent cultures and historical backgrounds and using predictions from theprototypical model as a benchmark. However, researchers have increasingly noted that
institutions have a key role in determining differences in the economic development
across countries (Acemoglu & Robinson, 2012).
Second, the internationally comparable education data initiated by TIMSS andPISA has been successful, allowing researchers to examine cross-country studies of theperformance of different educational institutions as factors that potentially determine
the economic performance (Hanushek & Woessman, 2015).
Third, as a logical consequence of the first and the second points, economists in the
United States started to direct more attention to the educational systems andfor the two countries in Ermisch, Frauke, & Spiess (2012b, Figures 5.3-4, pages 129–130) are available at https://www.russellsage.org/publications/parents-to-children.
13 Ermisch, Frauke, & Spiess (2012b, p.126), however, note that the German data used
only 13 items out of 20 SDQ questions to construct the difficulty score.
14 http://www.uis.unesco.org/Education/ISCEDMappings/Pages/default.aspx
10performance in other countries.
Current international comparisons of educational mobility and dynamics use
longitudinal data from different countries with different test items and survey
questionnaires. This has obvious limitations and potentially faces strong criticism. Our
analysis has made it clear that it is not possible to create perfect comparability acrosscountries using different national survey questionnaires and test items. However,
economists, while noticing weaknesses, tend to take an “incremental approach,” and“bravely” compare scores from different test items and variables from slightly different
questions. In other words, researchers working on international comparisons tend toplace more weight on the possibility of discovering a “Big Question” than minor
limitations and accuracy, leaving more detailed analysis for future research.
Researchers in this field still believe that the gain from imperfect comparison
outweighs the loss from no practice. For example, in a recent volume that collected
analyses of cross-country data for children, Ermisch, Jäntti, Smeeding, and Wilson(2012c) note that
A set of comparably designed national studies of this type can reveal how family
resources are correlated with individual outcomes at various points during theearly life course, and may be able to shed light on the structural differences thatmoderate intergenerational mobility in different ways in different countries.
Another advantage ... is that genetic transmission in the outcome (for example,
cognitive ability) should be the same across countries, and so cross-country
differences should reflect different environments, policy and otherwise. (Ermisch
et al. 2012c, p.11)
5. Agenda for Future Research in JapanFor Japanese education researchers, what are the positive purposes forchallenging international comparisons? This question may best be answered byconsidering three separate stages.
As in any comparative study, the first step is to incorporate outside perspectives ofthe state of education in Japan. There have been many studies for this purpose (Omomo
Inoguchi, Ueda, & Uesugi, 2007; Shimizu & Yamada, 2015). The second step is toattract non-Japanese researchers to undertake quantitative research on Japanese
education. Japan should be an attractive country to study as it is continuously ranked
in the top-tier of international educational achievement tests. By attracting
international researchers, Japanese researchers learn about recent quantitative11

methods, which in turn, stimulate policy makers to create new datasets andexperimental policies. The third stage is to share the quantitative evaluation ofJapanese education and policies with other countries, and thereby to use it to improveeducation policies in Japan and other countries. Japan has not yet accomplished thesecond stage.
Several previous studies have noted the uniqueness and effectiveness of
non-Western education systems and teaching styles. Until the mid-1990s, Japan
attracted many education researchers with its distinctive education tradition in family
and school organization (Lewis, 1995; Stevenson & Stigler, 1992; Tobin, Wu, & Davidson,1989). However, these have been predominantly studied with anthropological orparticipatory methods, and their influence is limited to open-minded educational
practitioners. Often, they are treated as a good “case study” that provides somereflections for traditional Western education practices, but not overarching policies.
Recent trends in the United States, backed by a growing awareness of institutions
and comparable data, are making an impact on countries previously overlooked in the
research community. The OECD has published several detailed reports using PISA datathat compared the results of selected high-performing countries (OECD, 2012). In
addition, journalists have started to take serious note of previously neglected countries
such as Poland (Ripley, 2013).
Globalization is expected to accelerate through the power of internet and big data,
and the international research and policy community has assumed the use of theinternet and easy access to education big data. In the era of quantitative research, withthe exception of some notable publications (OECD, 2011; 2012), Japan seems to have
faded from international comparison of the performance of education policies.
Our discussion suggests the following steps to achieve the second stage of the
globalization of education research.
1.

Education research in Japan should use language that is common andinternationally interpretable.

2.

Data and general information about Japanese school and education policies should
be made easily accessible in English on the internet.

3.

More weight should be placed on the use of economic frameworks, data, andquantitative methods as a common language in education research and policy
debates.
Recently, education researchers have criticized the state of Japanese education12

research for its frequent use of technical terms and concepts that are not easily sharedwith other fields. 15 A similar argument can be applied for international education
research collaboration. Japan remains an “invisible” country in terms of education
research in the internet era, because of the paucity of basic information about education
policies, laws, and states of schools available in English. For example, the official
English language websites for governmental education policies often lack informationcompared with the official Japanese websites.16 Many PISA reports have highlighted
that basic economic and quantitative methods play the role of a common language toevaluate the state of education and policies in different countries. There is much to begained for education researchers and policy makers if they acquire basic knowledge of
economic frameworks and statistical modeling.
Our study of economic and educational mobility using JCPS data highlights theimportance and challenges for the Japanese research community in participating ininternational comparisons in the economics of education. The JCPS is in the early stage,
and there is much room for improvement to allow better comparability withinternational data. We wish to continue to make this database larger and more reliable
for the research of economic and educational mobility, which will involve more people
from different fields. However, it should also be noted that the data is just one of thenecessary conditions. To compare and share the strength and weakness of the Japanese
education system with the global community, further movement of the researchcommunity and policy makers toward globalization is necessary.

Hirota (2007) criticized education research in Japan as using jargon difficult for
professionals in other fields to understand.
16 As of the time of writing (September 5, 2015), the official English site of Japan’s
Ministry of Education, Culture, Sports, Science and Technology(http://www.mext.go.jp/english/) had 809 pages, compared with the total 208,000 pages
on the official Japanese website (http://www.mext.go.jp/). The Tokyo Metropolitan Boardof Education, the district with the largest non-Japanese population, had only 28 English
pages of the total 19,300 pages.
15

13

References
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16Figure 1. Great Gatsby Curve

Source: Reproduced from Corak (2013) with permission.
Figure 2. Timeline of the Japan Child Panel Survey (JCPS)

Source: Akabayashi, Naoi, & Shikishima (forthcoming).

17

Figure 3. Dynamics of Japanese and mathematics Z-scores for the top and bottom
family income classes.
(1) Japan Child Panel Survey, by income quartile(a) Japanese(b) Mathematics

18

(2) United Kingdom, by income quintiles(3) United States, by income quintiles

Source: (1) Akabayashi, Naoi, & Shikishima (forthcoming). (2) and (3) Authors
calculation based on Magnuson, et al. (2012, Table 10.2-3, 10.8-9, pages 246–247, 254–
255).
Note: Data for Japan is the Japan Child Panel Survey (JCPS), data for the United
Kingdom are drawn from the Avon Longitudinal Study of Parents and Children(ALSPAC), and data for the United States are drawn from the Early Childhood
Longitudinal Study, Kindergarten Cohort (ECLS-K). The vertical axis measures thez-score of cognitive test outcomes.

19Figure 4. Transition matrices of composite cognitive test score in Japan, United
Kingdom, and Australia(1) Transition from first (bottom) quartile of cognitive test score(2) Transition from fourth (top) quartile of cognitive test score

Source: Authors’ calculation based on the Japan Child Panel Survey and Blanden etal. (2012, Figure 6.1, p.149). United Kingdom (composite cognitive test scores basedon the Millennium Cohort Study) Australia (composite cognitive test scores based on
the Longitudinal Study of Australian Children). The vertical axis measures thetransition probability in percentage.
20

Figure 5. Problematic behavior (difficulty score) by parental education and income
quartiles in Japan(1) By parental education
0.80

Standard Deviations

0.60
0.40
0.20
0.00
-0.20
-0.40

0-2 (Low) Junior highgraduate

3-4 (Medium)
High school
graduate

5b (Medium5a/6 (High) High) Junior College graduate
college/Technicalor higher
college-0.60(2) By parental income quartiles
0.40Standard Deviations

0.30
0.20
0.10
0.00
-0.10
-0.20

First Quartile

Second Quartile

Third Quartile

Fourth Quartile

-0.30
-0.40

Source: Akabayashi et al. (forthcoming), calculated using the Japan Child Panel Survey.
Note: Ages of children were 7 to 8 years. The vertical axis measures the standard
deviation unit of the total difficulty score of Strengths and Difficulties Questionnaire(SDQ; positive value means more difficult).

21

Table 1. Number of observations of children and households, and the Japan Child Panel Survey response rate

Survey year

Survey ofHousehold

Number oftargethouseholds

Number ofcooperative
households

Response
rate byhousehold

Number oftargetchildren

Number ofcooperative
childrenResponse
rate bychildren

2010

JHPS

644

312

48.4%

959

467

48.7%

2011

KHPS

730

434

59.5%

1126

662

58.8%

2012

JHPS

595

342

57.5%

888

493

55.5%

2013

KHPS

808

453

56.1%

1242

709

57.1%

Source: Authors’ calculation using Japan Child Panel Survey (JCPS).
JHPS: Japan Household Panel Survey; KHPS: Keio Household Panel Survey

22